Three-dimensional image generation method and apparatus

ABSTRACT

A method and apparatus of generating a three-dimensional (3D) image are provided. The method of generating a 3D image involves acquiring a plurality of images of a 3D object with a camera, calculating pose information of the plurality of images based on pose data for each of the plurality of images measured by an inertial measurement unit, and generating a 3D image corresponding to the 3D object based on the pose information.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit under 35 USC 119(a) of ChinesePatent Application No. 201410854192.2, filed on Dec. 31, 2014, in theState Intellectual Property Office of the People's Republic of China andKorean Patent Application No. 10-2015-0137713 filed on Sep. 30, 2015, inthe Korean Intellectual Property Office, the entire disclosures of bothof which are incorporated herein by reference for all purposes.

BACKGROUND

1. Field

The following description relates to a three-dimensional (3D) imagegeneration method and apparatus, and to a method and apparatus forgenerating a 3D image using two-dimensional (2D) images.

2. Description of Related Art

A general method of converting two-dimensional (2D) images to athree-dimensional (3D) image may be performed by exclusive software. Ingeneral, 2D images may not include depth information used in a 3D imagegeneration. Thus, it is difficult to generate an actual 3D shape basedon such method when 2D images of a user face are converted to a 3Dimage. Also, in terms of the method, a shape represented by a 3D imagemay differ based on an arbitrary expression ability of a technician.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended beused as an aid in determining the scope of the claimed subject matter.

In one general aspect, a method of generating a three-dimensional (3D)image involves acquiring a plurality of images of a 3D object with acamera, calculating pose information of the plurality of images based onpose data for each of the plurality of images measured by an inertialmeasurement unit (IMU), and generating a 3D image corresponding to the3D object based on the pose information.

The calculating may involve classifying the plurality of images into atleast one image segment, calculating first pose information of images ineach image segment based on pose data for each of the images, andcalculating the pose information of the plurality of images based on thefirst pose information.

The calculating of the first pose information involves calculating afirst pose parameter comprising at least one of rotation angleinformation and parallel transference direction information ofneighboring images in each image segment, based on the pose data foreach of the images, calculating a second pose parameter by mergingneighboring first pose parameters, wherein the neighboring first poseparameters correspond to a single common image, and the second poseparameter comprises parallel transference scale information; andcalculating the first pose information based on the first pose parameterand the second pose parameter.

The calculating of the first pose parameter may involve calculating poseestimation result information of the neighboring images based on shortdistance feature correspondence information, the pose estimation resultinformation comprising the first pose parameter and short distancefeature correspondence information, the short distance featurecorrespondence information comprising a feature correspondence initialimage and feature correspondence information of images neighboring thefeature correspondence initial image in a first image segment comprisingthe feature correspondence initial image, and wherein the calculating ofthe second pose parameter comprises merging the neighboring first poseparameters based on long distance feature correspondence information andthe pose estimation result information, the long distance featurecorrespondence information comprising feature correspondence informationof images in the first image segment other than the featurecorrespondence initial image.

The calculating of the pose estimation result information may involveacquiring the short distance feature correspondence information,selecting the short distance feature correspondence information, andcalculating the pose estimation result information based on the selectedshort range feature correspondence information.

The merging of the neighboring first pose parameters may involveacquiring the long distance feature correspondence information,calculating first 3D point information of the neighboring first poseparameters based on the long distance feature correspondenceinformation, determining 3D point information acquired through the poseestimation result information to be second 3D point information, andmerging the neighboring pose parameters based on the first 3D pointinformation and the second 3D point information.

The calculating of the pose information of the plurality of images mayfurther involve calculating a third pose parameter of the plurality ofimages by merging neighboring first pose parameters included indiffering image segments, wherein the neighboring first parametersincluded in the differing image segments correspond to an image sharedby the differing image segments, and the third pose parameter comprisesparallel transference scale information; and calculating the poseinformation of the plurality of images based on the first poseinformation and the third pose parameter.

The calculating of the pose information of the plurality of images mayfurther involve calculating a third pose parameter of the plurality ofimages by merging neighboring first pose parameters included indiffering image segments, wherein the neighboring first pose parametersincluded in the differing image segments correspond to an image sharedby the differing image segments, and the third pose parameter comprisesparallel transference scale information; and calculating the poseinformation of the plurality of images based on the first poseinformation and the third pose parameter, and wherein the calculating ofthe third pose parameter comprises: determining third 3D pointinformation and fourth 3D point information, each corresponding to theneighboring first pose parameters included in the differing imagesegments among the second 3D point information indicating the 3D pointinformation acquired through the pose estimation result information; andcalculating a third pose parameter of the plurality of images by mergingthe neighboring first pose parameters included in the differing imagesegments based on the third 3D point information and the fourth 3D pointinformation.

The generating may involve refining the pose information; and generatingthe 3D image based on the refined pose information.

The generating involves generating surface model information of theobject based on the pose information, and generating the 3D image basedon the surface model information.

The generating of the surface model information may involve performing astereo matching based on the pose information, acquiring depth imageinformation of the object based on a result of the stereo matching, andgenerating the surface model information based on the depth imageinformation.

The generating of the surface model information may involve generatingfirst surface model information of the object based on the poseinformation; and generating the surface model information by refiningthe first surface model information.

The generating of the surface model information by refining the firstsurface model information may involve refining the first surface modelinformation based on template model information of the object.

The refining of the first surface model information based on thetemplate model information may involve at least one of: removinginformation not corresponding to the template model information from thefirst surface model information; removing a first value of the firstsurface model information in response to a difference between the firstvalue and a second value of the template model information correspondingto the first value being greater than a preset threshold; and addinginformation to the first surface model information.

The acquiring of the plurality of images may involve acquiring theplurality of images by extracting images from a plurality of initialimages of the 3D object based on a preset image extraction method.

The preset image extraction method may involve at least one of a methodof extracting an image based on a quality of the image, a method ofextracting an image based on pose data of the image, and a method ofextracting an image based on a photographing time of the image.

The acquiring of the plurality of images by extracting the images mayinvolve: setting the extracted images as candidate images, determining afront image corresponding to a front side of the object among thecandidate images, determining an image having a preset rotation anglerelative to the front image to be a side image among the candidateimages, and acquiring the plurality of images based on the front imageand the side image.

The acquiring of the plurality of images may involve classifying theplurality of images into at least one image segment. The neighboringimage segments may share at least one image.

The object may be a face.

In another general aspect, a terminal includes a camera configured toacquire a plurality of images of a three-dimensional (3D) object, and aprocessor configured to calculate pose information of the plurality ofimages based on pose data for each of the plurality of images measuredby an inertial measurement unit (IMU) and generate a 3D imagecorresponding to the 3D object based on the pose information.

The plurality of images may include two-dimensional (2D) images of the3D object.

In another general aspect, a method of generating a three-dimensional(3D) image involves acquiring a plurality of images of a 3D object withan electronic device, and generating a 3D image corresponding to the 3Dobject by obtaining information regarding a movement of the electronicdevice via a sensor included in the electronic device to calculate poseinformation of the plurality of images.

The electronic device may be a terminal comprising a camera, and thesensor may be an inertial measurement unit of the terminal.

The generating of the 3D image may involve determining an image amongthe plurality of images that most closely correspond to a front image ofthe 3D object based on symmetry.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a method of generating athree-dimensional (3D) image based on two-dimensional (2D) images.

FIG. 2 is a block diagram illustrating an example of a 3D imagegeneration apparatus.

FIG. 3 is a flowchart illustrating an example of a 3D image generationmethod.

FIG. 4 is a flowchart illustrating an example of a method of acquiring aplurality of images of an object.

FIG. 5 illustrates an example of a method of determining a front imageand a side image.

FIG. 6 is a flowchart illustrating an example of a method of calculatingpose information of a plurality of images.

FIG. 7 illustrates an example of image segments.

FIG. 8 is a flowchart illustrating an example of a method of calculatingfirst pose information of images.

FIG. 9 is a flowchart illustrating an example of a method of a firstpose parameter of images.

FIG. 10 is a flowchart illustrating an example of a method ofcalculating a second pose parameter.

FIG. 11 illustrates an example of short distance feature correspondenceinformation and long distance feature correspondence information.

FIG. 12 is a flowchart illustrating another example of a method ofcalculating first pose information of images.

FIG. 13 is a flowchart illustrating an example of a method ofcalculating a third pose parameter.

FIG. 14 is a flowchart illustrating an example of a method of generatinga 3D image of an object.

FIG. 15 is a flowchart illustrating another example of a method ofgenerating a 3D image of an object.

FIG. 16 is a flowchart illustrating an example of a method of generatingsurface model information.

FIG. 17 is a flowchart illustrating another example of a method ofgenerating surface model information.

FIG. 18 is a block diagram illustrating another example of a 3D imagegeneration apparatus.

FIG. 19 is a block diagram illustrating yet another example of a 3Dimage generation apparatus.

Throughout the drawings and the detailed description, the same referencenumerals refer to the same elements. The drawings may not be to scale,and the relative size, proportions, and depiction of elements in thedrawings may be exaggerated for clarity, illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent to one of ordinary skill inthe art. The sequences of operations described herein are merelyexamples, and are not limited to those set forth herein, but may bechanged as will be apparent to one of ordinary skill in the art, withthe exception of operations necessarily occurring in a certain order.Also, descriptions of functions and constructions that are well known toone of ordinary skill in the art may be omitted for increased clarityand conciseness.

The features described herein may be embodied in different forms, andare not to be construed as being limited to the examples describedherein. Rather, the examples described herein have been provided so thatthis disclosure will be thorough and complete, and will convey the fullscope of the disclosure to one of ordinary skill in the art.

Hereinafter, reference will now be made in detail to examples withreference to the accompanying drawings, wherein like reference numeralsrefer to like elements throughout.

Various alterations and modifications may be made to the examples. Here,the examples are not construed as limited to the disclosure and shouldbe understood to include all changes, equivalents, and replacementswithin the idea and the technical scope of the disclosure.

The terminology used herein is for the purpose of describing particularexamples only and is not to be limiting of the examples. As used herein,the singular forms “a”, “an”, and “the” are intended to include theplural forms as well, unless the context clearly indicates otherwise. Itwill be further understood that the terms “include/comprise” and/or“have” when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, components, and/orcombinations thereof, but do not preclude the presence or addition ofone or more other features, numbers, steps, operations, elements,components, and/or groups thereof.

Unless otherwise defined, all terms including technical and scientificterms used herein have the same meaning as commonly understood by one ofordinary skill in the art to which examples belong. It will be furtherunderstood that terms, such as those defined in commonly-useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andwill not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

When describing the examples with reference to the accompanyingdrawings, like reference numerals refer to like constituent elements anda repeated description related thereto will be omitted. When it isdetermined detailed description related to a related known function orconfiguration they may make the purpose of the examples unnecessarilyambiguous in describing the examples, the detailed description will beomitted here.

FIG. 1 illustrates an example of a method of generating athree-dimensional (3D) image based on two-dimensional (2D) images.

The 2D image may be, for example, a digital image including a pluralityof pixels. Alternately, the image may refer to a frame.

According to one example, 2D images acquired by photographing an objectmay be used to generate a 3D image. The 3D image may be generated basedon 2D images acquired by photographing the object from a plurality ofviewpoints.

A correlation between 2D images may be calculated by analyzing andcomparing features of the 2D images. A 3D model of the object may begenerated based on the calculated correlation.

In the example illustrated in FIG. 1, the object is a head of anindividual that includes a face.

The 2D images may be obtained by, for example, a user photographing aface of a person from a plurality of viewpoints. The user may use acamera included in a portable terminal to photograph the face. A camerarefers to a hardware device that takes pictures or video. In theillustrated example, the 2D images 111 through 115 of the object isgenerated by a portable terminal.

In this example, a processor of the portable terminal calculates afeature correspondence relationship of the 2D images 111 through 115.The portable terminal may generate a surface 120 of the object based onthe feature correspondence relationship.

In this example, the portable terminal generates a 3D image 130 of theobject based on the surface 120. For example, the portable terminalperforms texturing on the surface 120.

Although the portable terminal is used to generate the 2D image in theaforementioned example, the present disclosure is not limited thereto.

In an example, the 3D image 130 is generated by additionally using posedata of the portable terminal acquiring the 2D images 111 through 115.The pose data is measured by, for example, an inertial measurement unit(IMU) and an inertial sensor included in the portable terminal.

The 3D image 130 includes information regarding the 3D coordinates offacial surface of a user, which has a variety of applicability. Forexample, the 3D image 130 may be displayed back to the user, using adisplay of the portable terminal so that the face is rendered from adifferent view point. In one example, the processor of the portableterminal may construct a virtual representation of the user withdifferent facial expression. The virtual representation can be appliedto a computer game, and displayed back to the user via the portableterminal or displayed in a portable terminal of a friend of the user.Various other ways of applying the 3D image to a portable terminal, anelectronic device, game console, and the like are possible.

Descriptions related to a method of generating the 3D image based on thepose data will be described with reference to FIGS. 2 through 10.

FIG. 2 is a block diagram illustrating a 3D image generation apparatus200.

The 3D image generation apparatus 200 may be a terminal. The 3D imagegeneration apparatus 200 may be, for example, a portable terminal, apersonal computer (PC), and a laptop computer. However, the 3D imagegeneration apparatus 200 is not limited to these examples; otherapparatus that is configured to process a photographed image may serveas the 3D image generation apparatus 200.

The 3D image generation apparatus 200 includes a camera 210, a processor220, a storage 230, and a display 240. Hereinafter, the 3D imagegeneration apparatus 200 is also referred to as an apparatus 200.

In this example, the camera 210 generates a digital image byphotographing an actual object.

The processor 220 is, for example, a hardware processor included in theapparatus 200. The processor 220 controls the camera 210 and the storage230. The processor 220 processes an image generated by the camera 210and processes data in the storage 230.

The processor 220 controls the camera 210 to process the image.

The storage 230 stores the image generated by the camera 210 and storesthe data processed by the processor 220.

The display 240 displays the image taken by the camera 210, the surface120 of the object generated by the 3D image generation apparatus 200,the 3D image 130 generated by the 3D image generation apparatus 200, andthe like. The display described herein may be implemented using a liquidcrystal display (LCD), a light-emitting diode (LED) display, a plasmadisplay panel (PDP), a screen, a touch screen or any other type ofdisplay known to one of ordinary skill in the art. A screen refers to aphysical structure that includes one or more hardware components thatprovide the ability to render an image or a user interface.

The screen may include any combination of a display region, a gesturecapture region, a touch-sensitive display, and a configurable area. Thescreen may be part of an apparatus, or may be an external peripheraldevice that is attachable to and detachable from the apparatus. Thedisplay may be a single-screen display or a multi-screen display.

Descriptions related to the camera 210, the processor 220, and thestorage 230 will be provided further with reference to FIGS. 3 through19.

FIG. 3 illustrates an example of a 3D image generation method.

In operation 310, a camera 210 acquires a plurality of images of anobject by photographing the object.

Descriptions related to a method of acquiring the plurality of imageswill be provided further with reference to FIGS. 4 and 5.

In operation 320, the processor 220 calculates pose information of theplurality of images.

According to one example, the pose information refers to a correlationbetween the plurality of images. For example, the processor 220calculates the pose information based on pose data for each of theplurality of images.

Descriptions related to a method of calculating the pose informationwill be described further with reference to FIGS. 6 through 13.

In operation 330, the processor 220 generates a 3D image of the objectbased on the pose information.

Descriptions related to a method of generating the 3D image of theobject will be described further with reference to FIGS. 14 through 17.

FIG. 4 illustrates an example of a method of acquiring a plurality ofimages of an object.

In this example, the operation 310 of acquiring the plurality of imageincludes operations 410 through 450.

In operation 410, the processor 220 extracts images from a plurality ofinitial images acquired by photographing an object. The plurality ofinitial images may be acquired by the camera 210.

In an example, a user photographs a face of a person using the camera210. The camera 210 generates 3D head portrait images.

As an example, to photograph an image, the user may move the portableterminal from one side to another side, for example, from a left ear toa right ear, based on a head as an axis while holding the portableterminal in a hand. The photographed image may include a plurality ofinitial images of the head. For example, to obtain the plurality ofinitial 2D images of a user, the user may use a front camera of aportable terminal to take a plurality of pictures of himself or herselffrom a plurality of angles.

A method of acquiring initial images is not limited to the foregoingexample.

In an example, each of the initial images may include a timestamp. Thetimestamp indicates a time at which each of the initial images isphotographed or generated.

In another example, each of the initial image may include pose data anda timestamp corresponding to the pose data. The pose data may be data ona pose of the portable terminal in a state in which each of the initialimages are photographed or generated. The pose data may be measuredusing, for example, an inertial sensor and an IMU included in theportable terminal. That is, the movement of the portable terminal may bemeasured by a sensor within the terminal to obtain the pose data.

The pose data indicates a direction of the portable terminalphotographing an initial image or a pose of the portable terminal. Forexample, the pose data may indicate a rotation angle of the portableterminal photographing the initial image. The processor 220 acquires thepose data from the portable terminal and match the pose data for eachinitial image based on a rotation matrix.

Since a method of acquiring the pose data using the inertial sensor istechnology known by those skilled in the art, repeated descriptions willbe omitted for increased clarity and conciseness.

The processor 220 acquires the pose data and perform a preprocessing onthe pose data. A timestamp of the initial image may differ from atimestamp of the pose data corresponding to the initial image. Forexample, the timestamp of the initial image may be earlier than thetimestamp of the pose data. In contrast, the timestamp of the pose datamay be earlier than the timestamp of the initial image.

The processor 220 performs the preprocessing by delaying the timestampof the initial image for a predetermined time to correlate thetimestamps. The predetermined time may be, for example, 200 milliseconds(ms).

In an example, the processor 220 extracts images from the plurality ofinitial images of the object based on a preset image extraction method.The preset image extraction method includes at least one of, forexample, a method of extracting an image based on a quality of theimage, a method of extracting an image based on pose data of the image,and a method of extracting an image based on a time at which the imageis photographed.

In operation 420, the processor 220 sets the extracted images ascandidate images.

In operation 430, the processor 220 determines a front imagecorresponding to a front side of the object among the candidate images.The front image may be determined to be a key image. Descriptionsrelated to the front image will be described further with reference toFIG. 5.

In operation 440, the processor 220 determines an image having a presetrotation angle relative to the front image to be a side image among thecandidate images. The side image may be determined to be a key image.Descriptions related to the side image will be described further withreference to FIG. 5.

In operation 450, the processor 220 acquires a plurality of images usedto generate a 3D image based on the front image and the side image.

In an example, the processor 220 determines images having a relativelyhigh quality to be the plurality of images based on qualities of thecandidate images. Candidate images having, for example, low motionartifact and higher definition may be determined to be the plurality ofimages.

In another example, the processor 220 determines the plurality of imagesbased on the pose data. Based on the pose data for each of the candidateimages, the processor 220 determines candidate images having, forexample, a rotation angle within a preset range to be the plurality ofimages.

In another example, the processor 220 determines the plurality of imagesbased on a time.

When a generation time of the candidate images is between 0 and 5seconds, candidate images corresponding to a generation time between 2and 4 seconds may be determined to be the plurality of images. Candidateimages generated in a middle period of time may have a higher qualitywhen compared to candidate image generated in an initial period of time.

In another example, the processor 220 determines a starting position andan ending position of the plurality of images based on at least one of avertical direction displacement and angular information acquired throughan IMU.

When a variation of an acquired pitch angle is smaller than a presetangle, the processor 220 may determine a current moment as the startingpoint of the plurality of images. When the variation of the acquiredpitch angle is larger than the preset angle, the processor 220 maydetermine the current moment as the ending position of the plurality ofimages. The preset angle may be, but not limited to, 5° or 7°.

In further another example, the processor 220 determines the currentmoment as the starting position of the plurality of images when avariation of an acquired vertical direction displacement is smaller thana preset width. The processor 220 may determine the current moment asthe ending position of the plurality of images when the variation of theacquired vertical direction displacement is larger than the presetwidth. The preset width may be, but not limited to, 1 centimeter (cm) or2 cm.

In terms of the method of determining the plurality of images among thecandidate images, the processor 220 selects an image including apredetermined region of the image. The predetermined region may be, forexample, the object. The processor 220 removes an image not includingthe object from the candidate images.

The processor 220 removes areas in each of the plurality of images otherthan an area corresponding to the object. As an example, the processor220 may remove background areas from the plurality of images such thateach of the plurality of images includes a face area only. When theplurality of images including only the area corresponding to the objectis used in a 3D image generation, a time for subsequent processing maybe reduced.

FIG. 5 illustrates an example of a method of determining a front imageand a side image.

The processor 220 determines a front image 510 corresponding to a frontside of an object among candidate images.

In this example, the processor 220 detects feature point information ofa predetermined image among the candidate images. To determine the frontimage, the processor 220 calculates a symmetry of a feature point. Forexample, the processor 220 may determine an image that is calculated tohave the most symmetric feature point to be the front image 510. Thefeature point may be, for example, a position of an eye to be detected,a location of the nose with respect to the eyes, and the like.

The processor 220 determines side images 520, 530, 540, and 550corresponding to sides of the object among the candidate images.

In an example, the processor 220 determines a candidate image having apreset rotation angle relative to the front image 510 to be the sideimage 520, 530, 540, or 550 based on pose data of the candidate images.The preset rotation angle may be, for example, −15°, 15°, −30°, and 30°.The side image may be an image with the least feature points.

In another example, the processor 220 determines a best image pair basedon an automatic algorithm.

FIG. 6 illustrates an example of a method of calculating poseinformation of a plurality of images.

Referring to FIG. 6, the operation 320 of calculating pose informationinvolves operations 610 through 630.

In operation 610, the processor 220 classifies a plurality of imagesinto at least one image segment. An image segment includes at least oneimage. Descriptions related to an image segment classification methodwill be described further with reference to FIG. 7.

In operation 620, the processor 220 calculates first pose information ofneighboring images in each image segment.

Descriptions related to the neighboring images will be described furtherwith reference to FIG. 7.

Descriptions related to a method of calculating the first poseinformation will be described further with reference to FIG. 8.

In operation 630, the processor 220 calculates pose information of theplurality of images based on the first pose information.

FIG. 7 illustrates examples of image segments.

Referring to FIG. 7, the images segments include a plurality of images11 through 19.

In this example, the processor 220 classifies the image segments suchthat each of the image segments includes three images. However, thenumber of images included in each image segment is not limited thereto.For instance, in n another example, the number of images may be greaterthan three or may be variable.

In this example, the processor 220 classifies the image segments suchthat neighboring image segments share an image. A first image segmentincludes the images 11 through 13. A second image segment includes theimages 13 through 15. The image 13 may be a common image shared by thefirst image segment and the second image segment.

As an example, the neighboring images of each image segment described inan example of FIG. 6 may be the images 11 and 12. As another example,neighboring images in each of the image segments may be the images 15and 16.

Also, the processor 220 classifies the image segments such that a keyimage is shared between two neighboring image segments.

FIG. 8 illustrates an example of a method of calculating first poseinformation of images.

The operation 620 of calculating first pose information of imagesinvolves operations 810 through 830.

In operation 810, the processor 220 calculates a first pose parameterincluding at least one of parallel transference information, paralleltransference direction information, and rotation angle information ofneighboring images in each image segment. For example, the processor 220calculates the first pose parameter of the neighboring images based onpose data of each of the neighboring images. Descriptions related to amethod of calculating the first pose parameter will be described furtherwith reference to FIG. 9.

In operation 820, the processor 220 calculates a second pose parameterby merging neighboring first pose parameters. The neighboring first poseparameters may correspond to a single common image. The second poseparameter includes parallel transference scale information, for example,parallel transference scale factor.

In operation 830, the processor 220 calculates first pose informationbased on the first pose parameter and the second pose parameter. Thefirst pose information may be pose information of all images included inan image segment. The first pose information includes at least one of,for example, the rotation angle information, the parallel transferenceinformation, and the parallel transference scale information.

FIG. 9 illustrates an example of a first pose parameter of images.

The operation 810 of calculating first pose parameter of neighboringimages includes an operation of calculating pose estimation resultinformation based on short distance feature correspondence.

Operation 810 of calculating first pose parameter of neighboring imagesincludes operations 910 through 930.

In operation 910, the processor 220 acquires short distance featurecorrespondence information from each image segment. The short distancefeature correspondence information is, for example, information on acorrespondence feature between a first image and second imagesneighboring the first image.

In an example, the processor 220 acquires the short distance featurecorrespondence information by extracting at least one feature pointmatching between the first image and the second images based on, forexample, an active contour model (ACM), an active shape model (ASM), anactive appearance model (AAM), and a supervised descent method (SDM).The short distance feature correspondence information includes aplurality of items of feature correspondence information.

Descriptions related to the short distance feature correspondenceinformation will be described further with reference to FIG. 11.

The processor 220 detects a feature point of neighboring images in animage segment. The processor 220 tracks a feature point of neighboringimages based on the detected feature point. The short distance featurecorrespondence information may be obtained by, for example, matching ortracking correspondence features of neighboring images.

The processor 220 detects the feature point by using, for example, anopen source computer vision library (OpenCV). Alternatively, theprocessor 220 detects the feature point based on features fromaccelerated segment test (FAST).

In operation 920, the processor 220 selects the short distance featurecorrespondence information.

In an example, the processor 220 selects the short distance featurecorrespondence information based on a pose estimation algorithm. Thepose estimation algorithm may be, for example, a random sample consensus(RANSAC).

Based on rotation angle information of neighboring images acquiredthrough the pose estimation and an IMU, the processor 220 estimatesparallel transference direction information of the neighboring images.

The processor 220 selects the short distance feature correspondenceinformation based on the pose estimation algorithm and the estimatedparallel transference direction information. The selected short distancefeature correspondence information may be, for example, featurecorrespondence information having a relatively high accuracy on theneighboring images.

By selecting the short distance feature correspondence information,reliable short distance feature correspondence information may beacquired.

In operation 930, the processor 220 calculates pose estimation resultinformation of the neighboring images based on the pose estimationalgorithm and the selected short distance feature correspondenceinformation. For example, the pose estimation result informationincludes a first pose parameter including at least one of rotation angleinformation and the parallel transference direction information.

FIG. 10 is a flowchart illustrating an example of a method ofcalculating a second pose parameter.

The operation 820 of calculating a second pose parameter involves anoperation of merging neighboring first pose parameters based on longdistance feature correspondence information and pose estimation resultinformation.

Operation 820 includes operations 1010 through 1040.

In operation 1010, the processor 220 acquires long distance featurecorrespondence information. The long distance feature correspondenceinformation is, for example, information on a correspondence featurebetween a first image and a third image that is not located adjacent tothe first image in one image segment.

In an example, the long distance feature correspondence information maybe obtained by extending a feature point acquired from the first imageand a second image neighboring the first image to match the third image.The feature point acquired from the first image and the second image maybe included in short distance feature correspondence information.

Since the long distance feature correspondence information is acquiredin each image segment only, an accuracy of feature correspondenceinformation may increase.

In operation 1020, the processor 220 calculates first 3D pointinformation of two neighboring first pose parameters in the imagesegment based on a triangularization algorithm and the long distancefeature correspondence information.

The first 3D point information may be expressed by a vertex having aspatial position.

In operation 1030, the processor 220 determines 3D point informationacquired based on the pose estimation result information to be second 3Dpoint information.

The second 3D point information may be expressed by a vertex having aspatial position.

In operation 1040, the processor 220 merges two neighboring first poseparameters based on the first 3D point information and the second 3Dpoint information, thereby generating a second pose parameter.

The merging of the two neighboring first pose parameters in an imagesegment may be performed to acquire parallel transference scaleinformation of the image segment. The processor 220 may generate thesecond pose parameter by multiplying one of parallel transferencevectors of the two neighboring first pose parameters by a scale factor.

As an example, a first 3D point of the two neighboring first poseparameters may be assumed as A, a second 3D point may be assumed as B,and a light center point of a common image of the two neighboring firstpose parameters may be assumed as O. In this example, the scale factormay be obtained by dividing a distance of AO by a distance of BO.

Additionally, when a plurality of neighboring first pose parameters ispresent in the image segment, a plurality of scale factors may becalculated. When the plurality of scale factors is calculated, theprocessor 220 may obtain a representative scale factor among theplurality of scale factors.

For example, the processor 220 may remove a scale factor calculated forA, B, and O which are not located on a common straight line. Theprocessor 220 may calculate an intermediate value of remaining scalefactors and determine the calculated intermediate value to be therepresentative scale factor.

FIG. 11 illustrates an example of short distance feature correspondenceinformation and long distance feature correspondence information.

Referring to FIG. 11, the image segments illustrated in FIG. 7 includesa plurality of images.

In this example, the image 11 and image 12 of the first image segmentare neighboring images used to acquire short distance featurecorrespondence information. The image 11 is also referred to as afeature correspondence initial image 11.

Short distance feature correspondence information 1110 may be featurecorrespondence information of the feature correspondence initial image11 and the image 12 neighboring the feature correspondence initial image11 in an image segment, for example, the first image segment, includingthe feature correspondence initial image 11.

Long distance feature correspondence information 1120 may be featurecorrespondence information of the images 12 and 13 in the first imagesegment aside from the feature correspondence initial image 11.

In an example, when the image 12 is the feature correspondence initialimage, the first image segment including three images, the images 11through 13, may not include the long distance feature correspondenceinformation of the image 12 corresponding to the feature correspondenceinitial image.

FIG. 12 illustrates another example of a method of calculating firstpose information of images.

The operation 620 of calculating first pose information of imagesinvolves operations 1210 and 1220.

In operation 1210, the processor 220 calculates a third pose parameterof a plurality of images by merging neighboring first pose parametersincluded in differing image segments. The third pose parameter includes,for example, parallel transference scale information.

The neighboring first pose parameters included in the differing imagesegments correspond to a common image shared by the differing imagesegments.

Descriptions related to a method of calculating the third pose parameterwill be described further with reference to FIG. 13.

In operation 1220, the processor 220 calculates pose information of theplurality of images based on first pose information and the third poseparameter.

The first pose information is calculated based on a first pose parameterand a second pose parameter in operation 830 as described above.

In an example, the pose information includes parallel transference scaleinformation, parallel transference direction information, and rotationangle information neighboring key images in each image segment. The poseinformation may also include parallel transference scale information ofthe plurality of images.

The processor 220 calculates the pose information of the plurality ofimages based on one of, for example, a 5-point algorithm, a 6-pointalgorithm, and an 8-point algorithm.

FIG. 13 illustrates an example of a method of calculating a third poseparameter.

Referring to FIG. 13, the operation 1210 of calculating third poseparameter involves operations 1310 and 1320.

In operation 1310, among second 3D point information, the processor 220determines third 3D point information and fourth 3D point informationcorresponding to neighboring first pose parameters, respectively. Inthis example, each of the neighboring first pose parameters is includedin a different image segment.

The neighboring first pose parameters included in the first imagesegment and the second image segment may be applicable as described withreference to FIG. 11. The first pose parameter included in the firstimage segment corresponds to the third 3D point information. The firstpose parameter included in the second image segment corresponds to thefourth 3D point information.

The second 3D point information includes 3D point information of eachimage segment acquired through a calculation of a pose estimationalgorithm. The processor 220 determines 3D point information of thefirst image segment acquired from the second 3D point information to bethe third 3D point information. The processor 220 determines 3D pointinformation of the second image segment acquired from the second 3Dpoint information to be the fourth 3D point information.

In operation 1320, the processor 220 calculates a third pose parameterof the plurality of images by merging the neighboring first poseparameters included in the differing image segments based on the third3D point information and the fourth 3D point information.

Since the foregoing descriptions related to the method of calculatingthe second pose parameter is also applicable to a method of calculatingthe third pose parameter, repeated descriptions will be omitted forincreased clarity and conciseness.

FIG. 14 illustrates an example of a method of generating a 3D image ofan object.

The operation 330 of generating a 3D image of the object involvesoperations 1410 and 1420.

In operation 1410, the processor 220 refines pose information. Forexample, the processor 220 refines the pose information by performing abundle adjustment processing on the pose information.

In operation 1420, the processor 220 generates a 3D image based on therefined pose information. Descriptions related to a method of generatingthe 3D image will be described further with reference to FIGS. 15through 17.

FIG. 15 illustrates another example of a method of generating a 3D imageof an object.

Referring to FIG. 15, the operation 330 of generating an 3D imageinvolves operations 1510 and 1520.

In operation 1510, the processor 220 generates surface model informationof an object based on pose information. Descriptions related to a methodof generating the surface model information will be described furtherwith reference to FIGS. 16 and 17.

In operation 1520, the processor 220 generates a 3D image of the objectbased on the surface model information.

FIG. 16 illustrates an example of a method of generating surface modelinformation.

The operation 1510 of generating surface model information involvesoperations 1610 through 1630.

In operation 1610, the processor 220 performs a stereo matching based onpose information. The stereo matching indicates, for example, anoperation of matching features of a plurality of images.

Since the stereo matching is technology known by those skilled in theart, repeated descriptions are omitted for increased clarity andconciseness.

In operation 1620, the processor 220 acquires information on a depthimage of the object based on the stereo matching.

In operation 1630, the processor 220 generates surface model informationof the object based on the information of the depth image.

FIG. 17 illustrates another example of a method of generating surfacemodel information.

Referring to FIG. 17, the operation 1510 of generating surface modelinformation involves operations 1710 and 1720.

In operation 1710, the processor 220 generates first surface modelinformation of an object based on pose information.

The processor 220 generates the first surface model information based ona depth image. For example, surface model information generated inoperation 1630 may be the first surface model information.

In operation 1720, the processor 220 generates surface model informationof the object by refining the first surface model information.

The processor 220 refines the first surface model information based ontemplate model information of the object. The processor 220 aligns atemplate model in a front image. The processor 220 aligns the templatemodel based on a feature point of the template model and a feature pointof the front image.

As an example, the processor 220 removes information not correspondingto the template model information from the first surface modelinformation based on the template model information.

As another example, when a difference between a first value of the firstsurface model information and a second value of the template modelinformation corresponding to the first value is greater than a presetthreshold, the processor 220 removes the first value.

As another example, the processor 220 adds information to the firstsurface model information based on the template model information.

Alternatively, the processor 220 acquires first cylindrical projectioninformation by performing a cylindrical projection on the alignedtemplate model information. The processor 220 acquires texturecoordinate information corresponding to the first cylindrical projectioninformation.

The processor 220 generates second cylindrical projection information byrefining the first cylindrical projection information. The secondcylindrical projection information may be the surface model informationof the object.

As an example, the processor 220 removes information not correspondingto the first cylindrical projection information from the secondcylindrical projection information.

As another example, the processor 220 removes information of which adifference from the first cylindrical projection information is greaterthan a preset threshold, from the second cylindrical projectioninformation.

As another example, the processor 220 inserts additional information inthe second cylindrical projection information based on a gradient valueof the first cylindrical projection information.

The processor 220 performs a smoothness processing on the secondcylindrical projection information.

After operation 1720 is performed, the processor 220 generates a 3Dimage based on the surface model information in operation 1520.

In an example, the processor 220 generates texture information of thesurface model information.

The processor 220 acquires two differing side images of the object andmaps the two differing side images on a plane having texturecoordinates, thereby acquiring two texture maps. The texture coordinatesmay be acquired by performing the cylindrical projection on the firstsurface model information.

The processor 220 performs an adjustment on illuminations and colors ofthe two texture maps. The processor 220 joints the two adjusted texturemaps to acquire one texture map.

The processor 220 generates a 3D image based on the acquired texturemap.

FIG. 18 is a block diagram illustrating a 3D image generation apparatus1800.

The 3D image generation apparatus 1800 corresponds to the apparatus 200.

The 3D image generation apparatus 1800 includes an acquirer 1810, acalculator 1820, and an image generator 1830. The acquirer 1810, thecalculator 1820, and the image generator 1830 may include one or moreprocessor and memory.

The acquirer 1810 acquires a plurality of images of an object. Forexample, the plurality of images may be obtained from a camera,retrieved from a buffer memory or obtained from a data storage.

The calculator 1820 calculates pose information of the plurality ofimages. The pose information may be stored in a memory.

The image generator 1803 generates a 3D image based on the poseinformation.

In an example, the acquirer 1810 may include an extractor 1811configured to extract a portion of initial images of the object to becandidate images based on a preset extraction method, a determiner 1812configured to determine a front image and a side image among thecandidate images, and an image sequence generator 1813 configured toacquire a plurality of images based on the determined front image andside image.

The calculator 1820 may include a calculating module 1821 configured tocalculate first pose parameters of neighboring images in each imagesegment, a merging module 1822 configured to generate a second poseparameter by merging first pose parameters of two neighboring images,and a pose information generating module 1823 configured to generatefirst pose information based on the first pose parameters and the secondparameter.

The calculating module 1821 may include a first acquiring sub-module18211 configured to acquire short distance feature correspondenceinformation, a selecting sub-module 18212 configured to select the shortdistance feature correspondence information, and a first calculatingsub-module 18213 configured to pose estimation result information of theneighboring images based on the selected short distance featurecorrespondence information.

The merging module 1822 may include a second acquiring sub-module 18221configured to acquire long distance correspondence information, a secondcalculating sub-module 18222 configured to calculate first 3D pointinformation of the first pose parameters of the two neighboring imagesbased on the long distance feature correspondence information, and amerging sub-module 18223 configured to merge the first pose parametersbased on the first 3D point information and second 3D point information.

The image generator 1830 may include a stereo matching module 1831configured to generate depth image information by performing a stereomatching based on the pose information, a first model generating module1832 configured to generate surface model information based on the depthimage information, a second model generating module 1833 configured togenerate first surface model information of the object, and a refiningmodule 1834 configured to generate the surface model information of theobject by refining the first surface model information. The generatedsurface model information may be stored in a memory.

FIG. 19 is a block diagram illustrating still another example of a 3Dimage generation apparatus.

An electronic device 1900 corresponds to the apparatus 200.

The electronic device 1900 includes a processor 1910, for example, acentral processing unit (CPU), at least one of an output interface 1920and a user interface 1930, a memory 1940, and at least one communicationbus 1950.

For example, the processor 220 corresponds to the processor 1910, thecamera 210 corresponds to the at least one user interface 1930, and thememory 1940 corresponds to the storage 230.

The apparatuses, units, modules, devices, and other componentsillustrated in FIGS. 2 and 12 that perform the operations describedherein with respect to FIGS. 3, 4, 6, 8-10 and 13-17 are implemented byhardware components. Examples of hardware components includecontrollers, sensors, generators, drivers, memories, comparators,arithmetic logic units, adders, subtractors, multipliers, dividers,integrators, and any other electronic components known to one ofordinary skill in the art. In one example, the hardware components areimplemented by computing hardware, for example, by one or moreprocessors or computers. A processor or computer is implemented by oneor more processing elements, such as an array of logic gates, acontroller and an arithmetic logic unit, a digital signal processor, amicrocomputer, a programmable logic controller, a field-programmablegate array, a programmable logic array, a microprocessor, or any otherdevice or combination of devices known to one of ordinary skill in theart that is capable of responding to and executing instructions in adefined manner to achieve a desired result. In one example, a processoror computer includes, or is connected to, one or more memories storinginstructions or software that are executed by the processor or computer.Hardware components implemented by a processor or computer executeinstructions or software, such as an operating system (OS) and one ormore software applications that run on the OS, to perform the operationsdescribed herein with respect to FIGS. 3, 4, 6, 8-10 and 13-17. Thehardware components also access, manipulate, process, create, and storedata in response to execution of the instructions or software. Forsimplicity, the singular term “processor” or “computer” may be used inthe description of the examples described herein, but in other examplesmultiple processors or computers are used, or a processor or computerincludes multiple processing elements, or multiple types of processingelements, or both. In one example, a hardware component includesmultiple processors, and in another example, a hardware componentincludes a processor and a controller. A hardware component has any oneor more of different processing configurations, examples of whichinclude a single processor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 3, 4, 6, 8-10 and 13-17 that performthe operations described herein with respect to FIGS. 1, 7 and 11 areperformed by a processor or a computer as described above executinginstructions or software to perform the operations described herein.

Instructions or software to control a processor or computer to implementthe hardware components and perform the methods as described above arewritten as computer programs, code segments, instructions or anycombination thereof, for individually or collectively instructing orconfiguring the processor or computer to operate as a machine orspecial-purpose computer to perform the operations performed by thehardware components and the methods as described above. In one example,the instructions or software include machine code that is directlyexecuted by the processor or computer, such as machine code produced bya compiler. In another example, the instructions or software includehigher-level code that is executed by the processor or computer using aninterpreter. Programmers of ordinary skill in the art can readily writethe instructions or software based on the block diagrams and the flowcharts illustrated in the drawings and the corresponding descriptions inthe specification, which disclose algorithms for performing theoperations performed by the hardware components and the methods asdescribed above.

The instructions or software to control a processor or computer toimplement the hardware components and perform the methods as describedabove, and any associated data, data files, and data structures, arerecorded, stored, or fixed in or on one or more non-transitorycomputer-readable storage media. Examples of a non-transitorycomputer-readable storage medium include read-only memory (ROM),random-access memory (RAM), flash memory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs,CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs,BD-Rs, BD-R LTHs, BD-REs, magnetic tapes, floppy disks, magneto-opticaldata storage devices, optical data storage devices, hard disks,solid-state disks, and any device known to one of ordinary skill in theart that is capable of storing the instructions or software and anyassociated data, data files, and data structures in a non-transitorymanner and providing the instructions or software and any associateddata, data files, and data structures to a processor or computer so thatthe processor or computer can execute the instructions. In one example,the instructions or software and any associated data, data files, anddata structures are distributed over network-coupled computer systems sothat the instructions and software and any associated data, data files,and data structures are stored, accessed, and executed in a distributedfashion by the processor or computer.

As a non-exhaustive example only, a terminal as described herein may bea mobile device, such as a cellular phone, a smart phone, a wearablesmart device (such as a ring, a watch, a pair of glasses, a bracelet, anankle bracelet, a belt, a necklace, an earring, a headband, a helmet, ora device embedded in clothing), a portable personal computer (PC) (suchas a laptop, a notebook, a subnotebook, a netbook, or an ultra-mobile PC(UMPC), a tablet PC (tablet), a phablet, a personal digital assistant(PDA), a digital camera, a portable game console, an MP3 player, aportable/personal multimedia player (PMP), a handheld e-book, a globalpositioning system (GPS) navigation device, or a sensor, or a stationarydevice, such as a desktop PC, a high-definition television (HDTV), a DVDplayer, a Blu-ray player, a set-top box, or a home appliance, or anyother mobile or stationary device capable of wireless or networkcommunication.

While this disclosure includes specific examples, it will be apparent toone of ordinary skill in the art that various changes in form anddetails may be made in these examples without departing from the spiritand scope of the claims and their equivalents. The examples describedherein are to be considered in a descriptive sense only, and not forpurposes of limitation. Descriptions of features or aspects in eachexample are to be considered as being applicable to similar features oraspects in other examples. Suitable results may be achieved if thedescribed techniques are performed in a different order, and/or ifcomponents in a described system, architecture, device, or circuit arecombined in a different manner, and/or replaced or supplemented by othercomponents or their equivalents. Therefore, the scope of the disclosureis defined not by the detailed description, but by the claims and theirequivalents, and all variations within the scope of the claims and theirequivalents are to be construed as being included in the disclosure.

What is claimed is:
 1. A method of generating a three-dimensional (3D)image, the method comprising: acquiring a plurality of images of a 3Dobject with a camera; calculating pose information of the plurality ofimages based on pose data for each of the plurality of images measuredby an inertial measurement unit (IMU); and generating a 3D imagecorresponding to the 3D object based on the pose information.
 2. Themethod of claim 1, wherein the calculating comprises: classifying theplurality of images into at least one image segment; calculating firstpose information of images in each image segment based on pose data foreach of the images; and calculating the pose information of theplurality of images based on the first pose information.
 3. The methodof claim 2, wherein the calculating of the first pose informationcomprises: calculating a first pose parameter comprising at least one ofrotation angle information and parallel transference directioninformation of neighboring images in each image segment, based on thepose data for each of the images; calculating a second pose parameter bymerging neighboring first pose parameters, wherein the neighboring firstpose parameters correspond to a single common image, and the second poseparameter comprises parallel transference scale information; andcalculating the first pose information based on the first pose parameterand the second pose parameter.
 4. The method of claim 3, wherein thecalculating of the first pose parameter comprises calculating poseestimation result information of the neighboring images based on shortdistance feature correspondence information, the pose estimation resultinformation comprising the first pose parameter and short distancefeature correspondence information, the short distance featurecorrespondence information comprising a feature correspondence initialimage and feature correspondence information of images neighboring thefeature correspondence initial image in a first image segment comprisingthe feature correspondence initial image, and wherein the calculating ofthe second pose parameter comprises merging the neighboring first poseparameters based on long distance feature correspondence information andthe pose estimation result information, the long distance featurecorrespondence information comprising feature correspondence informationof images in the first image segment other than the featurecorrespondence initial image.
 5. The method of claim 3, wherein thecalculating of the pose information of the plurality of images furthercomprises: calculating a third pose parameter of the plurality of imagesby merging neighboring first pose parameters included in differing imagesegments, wherein the neighboring first parameters included in thediffering image segments correspond to an image shared by the differingimage segments, and the third pose parameter comprises paralleltransference scale information; and calculating the pose information ofthe plurality of images based on the first pose information and thethird pose parameter.
 6. The method of claim 4, wherein the calculatingof the pose information of the plurality of images further comprises:calculating a third pose parameter of the plurality of images by mergingneighboring first pose parameters included in differing image segments,wherein the neighboring first pose parameters included in the differingimage segments correspond to an image shared by the differing imagesegments, and the third pose parameter comprises parallel transferencescale information; and calculating the pose information of the pluralityof images based on the first pose information and the third poseparameter, and wherein the calculating of the third pose parametercomprises: determining third 3D point information and fourth 3D pointinformation, each corresponding to the neighboring first pose parametersincluded in the differing image segments among the second 3D pointinformation indicating the 3D point information acquired through thepose estimation result information; and calculating a third poseparameter of the plurality of images by merging the neighboring firstpose parameters included in the differing image segments based on thethird 3D point information and the fourth 3D point information.
 7. Themethod of claim 1, wherein the generating comprises: refining the poseinformation; and generating the 3D image based on the refined poseinformation.
 8. The method of claim 1, wherein the generating comprises:generating surface model information of the 3D object based on the poseinformation; and generating the 3D image based on the surface modelinformation.
 9. The method of claim 8, wherein the generating of thesurface model information comprises: performing a stereo matching basedon the pose information; acquiring depth image information of the 3Dobject based on a result of the stereo matching; and generating thesurface model information based on the depth image information.
 10. Themethod of claim 8, wherein the generating of the surface modelinformation comprises: generating first surface model information of the3D object based on the pose information; and generating the surfacemodel information by refining the first surface model information. 11.The method of claim 10, wherein the generating of the surface modelinformation by refining the first surface model information comprises:refining the first surface model information based on template modelinformation of the 3D object.
 12. The method of claim 11, wherein therefining of the first surface model information based on the templatemodel information comprises at least one of: removing information notcorresponding to the template model information from the first surfacemodel information; removing a first value of the first surface modelinformation in response to a difference between the first value and asecond value of the template model information corresponding to thefirst value being greater than a preset threshold; and addinginformation to the first surface model information.
 13. The method ofclaim 1, wherein the acquiring of the plurality of images comprisesacquiring the plurality of images by extracting images from a pluralityof initial images of the 3D object based on a preset image extractionmethod.
 14. The method of claim 13, wherein the preset image extractionmethod comprises at least one of a method of extracting an image basedon a quality of the image, a method of extracting an image based on posedata of the image, and a method of extracting an image based on aphotographing time of the image.
 15. The method of claim 13, wherein theacquiring of the plurality of images by extracting the images comprises:setting the extracted images as candidate images; determining a frontimage corresponding to a front side of the 3D object among the candidateimages; determining an image having a preset rotation angle relative tothe front image to be a side image among the candidate images; andacquiring the plurality of images based on the front image and the sideimage.
 16. The method of claim 1, wherein the acquiring the plurality ofimages comprises classifying the plurality of images into at least oneimage segment, and neighboring image segments share at least one image.17. The method of claim 1, wherein the 3D object comprises a face.
 18. Aterminal comprising: a camera configured to acquire a plurality ofimages of a three-dimensional (3D) object; and a processor configured tocalculate pose information of the plurality of images based on pose datafor each of the plurality of images measured by an inertial measurementunit (IMU) and generate a 3D image corresponding to the 3D object basedon the pose information.
 19. A method of generating a three-dimensional(3D) image, the method comprising: acquiring a plurality of images of a3D object with an electronic device; and generating a 3D imagecorresponding to the 3D object by obtaining information regarding amovement of the electronic device via a sensor included in theelectronic device to calculate pose information of the plurality ofimages.
 20. The method of claim 19, wherein the electronic device is aterminal comprising a camera, and the sensor is an inertial measurementunit of the terminal.